A Quantile-based Matching (QM) method has been widely used to correct the biases in global and regional climate model outputs. The basic idea of QM is to adjust the Cumulative Distribution Function (CDF) of model for the projection period on the basis of the difference between the model and observation CDFs for the training period. Therefore, the CDF of observation on training period plays an important role in quantile-based matching. Also, ensembles are highly correlated because ensemble forecasts generated from a combination of randomly perturbed initial conditions and different convective schemes in numerical weather model. We discuss the dependence of the bias correction results obtained from Qunatile-based Matching when there is correlation between ensembles and the variance of observation is larger than that of model. A simulation study is employed to understand the relation and distributional characteristics of observation and model when applying Quantile-based Matching method.

Abstract
 1. 서론
 2. 연구방법
 3. 연구결과
  1) 분위수-분위수 그림(Quantile-Quantile plot)
  2) 분위수 매칭
 4. 결론
 References

• 장동호(공주대학교 지리학과) | Dong-Ho Jang (Department of Geography, Kongju National University)
• 김찬수(공주대학교 응용수학과) | Chansoo Kim (Department of Applied Mathematics, Kongju National University) Correspondence